Vendors (“shippers”) supply goods to manufactures and/or service providers (“consignees”), which in turn become vendors delivering goods and/or services to further parties. The relationship of goods, often in the form of commodities, and the shippers and consignees forms a supply chain. One method of representing such a supply chain is in the form of a supply chain graph. Companies often lack an explicit graph representation of their own supply chain. Companies may lack sufficient data on incoming vendor and outgoing customer relationships to form a supply chain graph. Additionally, often such supply chain information is a closely guarded company secret, making such data inaccessible to third parties. A company would greatly benefit from knowing its competitors' supply chain information. Access to competitors'supply chain information allows a company to generate a supply chain representation or graph to illustrate and convey a robust and comprehensive understanding of current market risks and opportunities. Analysts can use supply chain graphs to better understand risk exposure implied in a given supply chain. For example, if Apple Corporation relies on lithium batteries to power its mobile computing devices, then a lack of lithium production in the mines where the element originates could lead to a bottleneck in Apple's product supplies, leading to revenue loss, and it could lead to the market price for lithium going up, thus cutting into the margin of devices sold. Both of these effects could directly or indirectly lead to a loss of profits for Apple and its shareholders as well as component suppliers.
Currently, in the context of supply chain management risk alerts with respect to entities and activities are known but are largely untimely and ineffective. Although companies may have access to internal data for the use in generating supply chain graphs for activities within the company, there is currently no effective process for accessing and analyzing data sources or utilities that a company can use to obtain or generate competitors' supply chain graphs. While data is available which may help a company assess current market risks and conditions, a complete and readily accessible data set is not available for a company wishing to analyze the supply chains of other companies. Also, there is no mechanism to arrive at a comprehensive supply chain representation across an industry or other select grouping of concerns. In order to perform a meaningful assessment of current and future market conditions, it is often necessary to compile not only sufficient information, but information of the proper type to formulate an accurate judgment as to whether the information constitutes a risk. Without the ability to access and assimilate a variety of different information sources, and particularly from a sufficient number and type of information sources, into a complete supply chain graph, the identification, assessment and communication of potential risks is significantly hampered. Currently, gathering of supply chain information is performed manually, resulting in inefficiencies and delays, and lacks defined criteria and processes for mining meaningful information to provide a clear picture of the supply chains of others in the market. The invention relates broadly to supply chain visual representations (“visualizations”). For purposes of explaining the applications of the invention in the discussion herein uses the term “graph” as illustrative of a common and preferred form of visual representation. However, the invention is not limited to graphical representation.
With the advents of the printing press, typeset, typewriting machines, computer-implemented word processing and mass data storage, the amount of information generated by mankind has risen dramatically and with an ever quickening pace. As a result of the growing and divergent sources of supply chain information, there is far more information available for creating supply chain visualizations, however manual processing of documents and the content therein is not possible or desirable. Accordingly, there exists a growing need to collect and store, identify, track, classify and catalogue, and process this growing sea of supply chain information/content and to deliver value added service to facilitate informed use of the data and predictive patterns derived from such supply chain information. Due to the development and widespread deployment of and accessibility to high speed networks, e.g., Internet, there exists a growing need to adequately and efficiently process the growing volume of content available on such networks to assist in decision making. In particular the need exists to quickly process information pertaining to supplier/commodity/customer relationships and events that may have an impact (positive or negative) on such relationships and commodity availability and flow so as to enable informed decision making in light of the effect of events and performance, including predicting the effect such events may have on pricing and availability of commodities in a supply chain.
In many areas and industries, including financial services sector, for example, there are content and enhanced experience providers, such as The Thomson Reuters Corporation, Wall Street Journal, Dow Jones News Service, Bloomberg, Financial News, Financial Times, News Corporation, Zawya, and New York Times. Such providers identify, collect, analyze and process key data for use in generating content, such as reports and articles, for consumption by professionals and others involved in the respective industries, e.g., financial consultants and investors. In one manner of content delivery, these financial news services provide financial news feeds, both in real-time and in archive, that include articles and other reports that address the occurrence of recent events that are of interest to investors. Many of these articles and reports, and of course the underlying events, may have a measureable impact on the pricing and availability of commodities. For example, a company may issue a press release that it (as supplier) has entered into an agreement with an other company (customer) to supply that company with a certain quantity of commodities, goods, or services (commodity). Professionals and providers in the various sectors and industries continue to look for ways to enhance content, data and services provided to subscribers, clients and other customers and for ways to distinguish over the competition. Such providers strive to create and provide enhance tools, including search and visualization tools, to enable clients to more efficiently and effectively process information and make informed decisions.
Advances in technology, including database mining and management, search engines, linguistic recognition and modeling, provide increasingly sophisticated approaches to searching and processing vast amounts of data and documents, e.g., database of news articles, financial reports, blogs, SEC and other required corporate disclosures, legal decisions, statutes, laws, and regulations, that may affect business performance, including pricing and availability of commodities. Investment and other financial professionals and other users increasingly rely on mathematical models and algorithms in making professional and business determinations. Especially in the area of investing, systems that provide faster access to and processing of (accurate) news and other information related to corporate operations performance will be a highly valued tool of the professional and will lead to more informed, and more successful, decision making Information technology and in particular information extraction (IE) are areas experiencing significant growth to assist interested parties to harness the vast amounts of information accessible through pay-for-services or freely available such as via the Internet.
Many financial services providers use “news analysis” or “news analytics,” which refer to a broad field encompassing and related to information retrieval, machine learning, statistical learning theory, network theory, and collaborative filtering, to provide enhanced services to subscribers and customers. News analytics includes the set of techniques, formulas, and statistics and related tools and metrics used to digest, summarize, classify and otherwise analyze sources of information, often public “news” information. An exemplary use of news analytics is a system that digests, i.e., reads and classifies, financial information to determine market impact related to such information while normalizing the data for other effects. News analysis refers to measuring and analyzing various qualitative and quantitative attributes of textual news stories, such as that appear in formal text-based articles and in less formal delivery such as blogs and other online vehicles. More particularly, the present invention concerns analysis in the context of electronic content. Expressing, or representing, news stories as “numbers” or other data points enables systems to transform traditional information expressions into more readily analyzable mathematical and statistical expressions and further into useful data structures and other work product. News analysis techniques and metrics may be used in the context of finance and more particularly in the context of investment performance—past and predictive.
News analytics systems may be used to measure and predict: volatility of commodity pricing and volatility and effects on markets; reversals of news impact; the relevance of risk-related words in annual reports for predicting negative or positive impact; and the impact of news stories on commodities. News analytics often views information at three levels or layers: text, content, and context. Many efforts focus on the first layer—text, i.e., text-based engines/applications process the raw text components of news, i.e., words, phrases, document titles, etc. Text may be converted or leveraged into additional information and irrelevant text may be discarded, thereby condensing it into information with higher relevance/usefulness. The second layer, content, represents the enrichment of text with higher meaning and significance embossed with, e.g., quality and veracity characteristics capable of being further exploited by analytics. Text may be divided into “fact” or “opinion” expressions. The third layer of news analytics—context, refers to connectedness or relatedness between information items. Context may also refer to the network relationships of news.
There are known methods for the preprocessing of data, entity extraction, entity linking, indexing of data, and for indexing ontologies. For example U.S. Pat. No. 7,333,966, entitled “SYSTEMS, METHODS, AND SOFTWARE FOR HYPERLINKING NAMES”, U.S. Pat. Pub. 2009/0198678, entitled “SYSTEMS, METHODS, AND SOFTWARE FOR ENTITY RELATIONSHIP RESOLUTION”, U.S. patent application Ser. No. 12/553,013, entitled “SYSTEMS, METHODS, AND SOFTWARE FOR QUESTION-BASED SENTIMENT ANALYSIS AND SUMMARIZATION”, U.S. Pat. Pub. 2009/0327115, entitled “FINANCIAL EVENT AND RELATIONSHIP EXTRACTION”, and U.S. Pat. Pub. 2009/0222395, entitled “ENTITY, EVENT, AND RELATIONSHIP EXTRACTION”, the contents of each of which are incorporated herein by reference herein in their entirety, describe systems, methods and software for the preprocessing of data, entity extraction, entity linking, indexing of data, and for indexing ontologies in addition to linguistic and other techniques for mining or extracting information from documents and sources.
What is needed is a system capable of automatically processing, parsing, or “reading” news stories, press releases, regulatory and other filings, and other content and sources of information available to it and quickly interpreting the content to identify individual data elements necessary to automatically generate a complete supply chain visualization. Presently, there exists a need to utilize and leverage media and other sources of entity information and a need for advanced analytics relevant to corporate performance, commodity availability and price behavior, investing, and awareness to generate supply chain visualizations. Given the vast amount of news, legal, regulatory and other entity-related information based on text, content and context, investors, corporations, and those involved in financial services have a persistent need and desire for an understanding of how such vast amounts of information, even processed information, relates to the movement of goods, services, and other commodities through supply chains of markets, industries, companies and competitors.